
import importlib

import torch
import yaml
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel




def       _SCREAMING_SNAKE_CASE   (						_lowerCamelCase					:     List[str]		,    _lowerCamelCase					:     Optional[int]=False)   ->       Union[str, Any]:






							'''simple docstring'''





							__UpperCamelCase      :  Any       =   OmegaConf.load(_lowerCamelCase)
							if display:
														print(yaml.dump(OmegaConf.to_container(_lowerCamelCase)))
							return config




def       _SCREAMING_SNAKE_CASE   (						_lowerCamelCase					:     Dict		,    _lowerCamelCase					:     int=None		,    _lowerCamelCase					:     List[str]=None)   ->       str:






							'''simple docstring'''





							if conf_path is None:
														__UpperCamelCase      :  Dict       =   "./model_checkpoints/vqgan_only.yaml"
							__UpperCamelCase      :  Dict       =   load_config(_lowerCamelCase		,    display=_lowerCamelCase)
							__UpperCamelCase      :  Any       =   VQModel(**config.model.params)
							if ckpt_path is None:
														__UpperCamelCase      :  List[str]       =   "./model_checkpoints/vqgan_only.pt"
							__UpperCamelCase      :  Any       =   torch.load(_lowerCamelCase		,    map_location=_lowerCamelCase)
							if ".ckpt" in ckpt_path:
														__UpperCamelCase      :  Optional[Any]       =   sd["state_dict"]
							model.load_state_dict(_lowerCamelCase		,    strict=_lowerCamelCase)
							model.to(_lowerCamelCase)
							del sd
							return model




def       _SCREAMING_SNAKE_CASE   (						_lowerCamelCase					:     Tuple		,    _lowerCamelCase					:     Dict)   ->       Optional[int]:






							'''simple docstring'''





							__UpperCamelCase  ,		__UpperCamelCase  ,		__UpperCamelCase      :  Any       =   model.encode(_lowerCamelCase)
							print(F'VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}')
							__UpperCamelCase      :  List[str]       =   model.decode(_lowerCamelCase)
							return xrec




def       _SCREAMING_SNAKE_CASE   (						_lowerCamelCase					:     List[Any]		,    _lowerCamelCase					:     Any=False)   ->       List[str]:






							'''simple docstring'''





							__UpperCamelCase  ,		__UpperCamelCase      :  Tuple       =   string.rsplit("."		,    1)
							if reload:
														__UpperCamelCase      :  Tuple       =   importlib.import_module(_lowerCamelCase)
														importlib.reload(_lowerCamelCase)
							return getattr(importlib.import_module(_lowerCamelCase		,    package=_lowerCamelCase)		,    cls)




def       _SCREAMING_SNAKE_CASE   (						_lowerCamelCase					:     Optional[Any])   ->       str:






							'''simple docstring'''





							if "target" not in config:
														raise KeyError("Expected key `target` to instantiate.")
							return get_obj_from_str(config["target"])(**config.get("params"		,    {}))




def       _SCREAMING_SNAKE_CASE   (						_lowerCamelCase					:     Optional[Any]		,    _lowerCamelCase					:     Tuple		,    _lowerCamelCase					:     Optional[Any]=True		,    _lowerCamelCase					:     Union[str, Any]=True)   ->       Optional[int]:






							'''simple docstring'''





							__UpperCamelCase      :  Any       =   instantiate_from_config(_lowerCamelCase)
							if sd is not None:
														model.load_state_dict(_lowerCamelCase)
							if gpu:
														model.cuda()
							if eval_mode:
														model.eval()
							return {"model": model}




def       _SCREAMING_SNAKE_CASE   (						_lowerCamelCase					:     int		,    _lowerCamelCase					:     Any		,    _lowerCamelCase					:     Union[str, Any]		,    _lowerCamelCase					:     List[str])   ->       Union[str, Any]:






							'''simple docstring'''





							if ckpt:
														__UpperCamelCase      :  Any       =   torch.load(_lowerCamelCase		,    map_location="cpu")
														__UpperCamelCase      :  List[str]       =   pl_sd["global_step"]
														print(F'loaded model from global step {global_step}.')
							else:
														__UpperCamelCase      :  int       =   {"state_dict": None}
														__UpperCamelCase      :  str       =   None
							__UpperCamelCase      :  Union[str, Any]       =   load_model_from_config(config.model		,    pl_sd["state_dict"]		,    gpu=_lowerCamelCase		,    eval_mode=_lowerCamelCase)["model"]
							return model, global_step

import math




def       _SCREAMING_SNAKE_CASE   (						_lowerCamelCase					:     int)   ->       str:






							'''simple docstring'''





							__UpperCamelCase      :  Union[str, Any]       =   0
							__UpperCamelCase      :  int       =   0
							while num > 0:
														__UpperCamelCase      :  List[Any]       =   num % 8
														__UpperCamelCase      :  Tuple       =   octal + (remainder * math.floor(math.pow(10		,    _lowerCamelCase)))
														counter += 1
														__UpperCamelCase      :  Optional[Any]       =   math.floor(num / 8)  # basically /= 8 without remainder if any
														# This formatting removes trailing '.0' from `octal`.
							return F'0o{int(_lowerCamelCase)}'




def       _SCREAMING_SNAKE_CASE   (						)   ->       None:






							'''simple docstring'''





							print("\n2 in octal is:")
							print(decimal_to_octal(2))  # = 2
							print("\n8 in octal is:")
							print(decimal_to_octal(8))  # = 10
							print("\n65 in octal is:")
							print(decimal_to_octal(65))  # = 101
							print("\n216 in octal is:")
							print(decimal_to_octal(216))  # = 330
							print("\n512 in octal is:")
							print(decimal_to_octal(512))  # = 1000
							print("\n")


if __name__ == "__main__":
		main()